collectively provide persistent and com- plete battlefield coverage as a defensive shield or sensing field, or function as a swarm or “wolf pack” to unleash a pow- erful coordinated attack.


In this vision of future ground warfare, a key challenge is to enable autono- mous systems and Soldiers to interact effectively and naturally across a broad range of warfighting functions. Human- agent collaboration is an active research area that addresses calibrated trust and transparency, common understanding of shared perceptions, and human-agent dialogue and collaboration. Army S&T is focused on the fundamental understand- ing and methods to design and develop future Army autonomous systems that will interact seamlessly with Soldiers.


One function with technology that has relied on a foundation of government research is question answering—the sys- tem’s ability to respond with relevant, correct information to a clearly stated question. Te recent question-answering successes of commercial technologies like IBM Watson and Apple’s Siri are based on several decades of government leader- ship in related research fields.


Tey work well for very large, stable and fairly accurate volumes of data, like ency- clopedias. But such tools don’t work for rapidly changing battlefield data, which can be distorted by adversaries’ con- cealment and deception. Commercial question-answering systems cannot sup- port continuous, meaningful dialogue in which both Soldiers and artificially intel- ligent agents develop shared situational awareness and intent understanding. Te Army is performing research to develop human-robotic dialogue technology for warfighting tasks, using natural voice, which is critical for reliable battlefield teaming.


FROM CHAOS, ORDER


Army AI and machine learning involve unique challenges for Soldiers, including operations in unstructured, unstable, rapidly changing, chaotic and adversarial environments where gathering information is difficult, and the information gathered may be potentially erroneous, misleading and deceptive. (Image courtesy of ARL)


Also


critical


is


the


self-organization


of robotic team members. By leverag- ing available commercial technologies like the Robotic Operating System and commercial robotic platforms, Army sci- entists are performing research to address Soldier-robotic teaming on complex ground terrain. For example, the Army recently demonstrated leader-follower driving of resupply trucks in which sev- eral unmanned vehicles autonomously follow a human-driven truck, on narrow forest roads with tree canopy, at tactically appropriate speed and with long gaps between the trucks.


When a team includes multiple arti- ficial agents, or when multiple


teams


must work together, new challenges arise: decentralized mission-level task


allocation; self-organization, adaptation, and collaboration; space management operations; and joint sensing and percep- tion. Commercial efforts to date have largely been limited to single platforms in benign settings. Within the Army, some programs like the U.S. Army Research Laboratory’s (ARL’s) Micro Autonomous Systems and Technology Collaborative Technology Alliance (MAST CTA) have been developing collaborative behaviors for unmanned aerial vehicles. Ground vehicle collaboration is challenging and is largely still at the basic research level. Te Army’s long-term focus is on enabling collaboration among large numbers of highly dissimilar entities, such as large and small teams of air and ground robots, as well as human Soldiers, distributed over a large contested environment. To


ASC.ARMY.MIL 93


SCIENCE & TECHNOLOGY / DASA(R&T)


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